HR needs metrics and analytics to retain star performers following EU plans to widen bonus caps
The EU recently published its plans to widen bonus caps for banks, but despite the hype that this will lead to loss of staff and competitive advantage, banks have already begun to make positive preparations for these changes.
Alternative incentive programmes focused on weighting reward in favour of long term equity instead of short term cash are being discussed, designed, and in a few cases, implemented.
However, worryingly, there is one area that has yet to be addressed that could lead to the undoing of any new reward packages implemented. And, unfortunately, responsibility rests squarely with HR: performance measurement.
Rebalancing reward towards equity will be positive for banks, encouraging a long term view to work and discouraging the kind of short term risk taking that led to the financial crisis in 2008.
However, moving away from bonuses towards higher base salaries and equity requires a complex way of measuring performance - metrics that for many companies don't currently exist. This means if new incentive programmes are implemented there will be a disconnect between bankers' performance and HR's ability to measure results and reward according to top performance.
Without HR analytics, measurement becomes a subjective "finger in the air" exercise based on networking and relationships within the company rather than on performance. This is likely to lead to employees leaving if they feel they are not being rewarded fairly - and if that includes top talent, HR is responsible for affecting the performance of the company.
To overcome this, companies need to be able to make informed people-decisions based on accurate data and structured analysis. Following the best practice framework below will help HR departments define the right approach to reward packages that are fair to the individuals, shareholders, and society alike:
Step 1: Frame the central problem - Interview key players in line management, HR, Finance, Operations and other functions as necessary, to build a perspective on the potential variables involved in creating a solution. Review and glean data from existing documents or systems that provide detailed information and overall context.
Step 2: Apply a conceptual model to guide the analysis - Identify workforce and business variables that are likely to have associations with the problem's outcome. Be alert to idiosyncratic events and additional data that could be relevant; develop a list of hypotheses.
Step 3: Capture relevant data - Pursue appropriate data across all relevant business units: HR, Operations, Finance and Marketing. Reconcile differences in definitions, codes, and time frames to ensure that data is relevant to the hypotheses.
Step 4: Apply analytical methods - Employ appropriate formal quantitative techniques, look for stable patterns over time. Examine results and identify robust explanatory models.
Step 5: Present statistical findings to stakeholders - Construct a presentation of results that is accessible to business managers without a statistical background. Validate and enrich statistical patterns with stakeholders' experience through interviews and focus group discussions.
Step 6: Define action steps to implement the solution - Operationalise changes in policies, procedures and management actions designed to produce desired changes in workforce behaviour. Monitor and document changes in management actions and workforce outcomes.
The reward packages designed by the banks in response to EU proposals could be a positive change for staff, but HR needs to get the right metrics and insights into the data so they can track, reward, and retain star performers to maintain a competitive advantage. Those that don't will risk undoing all the measures that tackle the EU plans to widen bonus caps. Until this is fixed, HR departments across all banks run the risk of being caught out once the new reward programmes are in place.
Tim Ringo (pictured) is UK market leader at consulting firm North Highland